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Electrostatic Field Classifier for Deficient Data.

Budka, M. and Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. In: Kurzynski, M. and Wozniak, M., eds. Computer Recognition Systems 3. Heidelberg: Springer, 311-318.

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Budka_Gabrys_EFC_for_deficient_data_CORES2009.PDF - Accepted Version


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DOI: 10.1007/978-3-540-93905-4_37


This paper investigates the suitability of recently developed models based on the physical field phenomena for classification problems with incomplete datasets. An original approach to exploiting incomplete training data with missing features and labels, involving extensive use of electrostatic charge analogy, has been proposed. Classification of incomplete patterns has been investigated using a local dimensionality reduction technique, which aims at exploiting all available information rather than trying to estimate the missing values. The performance of all proposed methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios and compared to the performance of some standard techniques. Several modifications of the original electrostatic field classifier aiming at improving speed and robustness in higher dimensional spaces are also discussed.

Item Type:Book Section
Series Name:Advances in Intelligent and Soft Computing
Number of Pages:612
Group:Faculty of Science & Technology
ID Code:9541
Deposited By: Professor Bogdan Gabrys LEFT
Deposited On:05 Feb 2009 14:17
Last Modified:14 Mar 2022 13:21


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